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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: plant-seedlings-model-beit-free-0-6 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7475442043222004 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# plant-seedlings-model-beit-free-0-6 |
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7557 |
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- Accuracy: 0.7475 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.4892 | 0.2 | 100 | 2.4909 | 0.0751 | |
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| 2.4906 | 0.39 | 200 | 2.4886 | 0.0756 | |
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| 2.3925 | 0.59 | 300 | 2.3344 | 0.1537 | |
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| 2.31 | 0.79 | 400 | 2.3306 | 0.1464 | |
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| 2.2355 | 0.98 | 500 | 2.2335 | 0.1778 | |
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| 2.2642 | 1.18 | 600 | 2.1889 | 0.1807 | |
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| 2.0806 | 1.38 | 700 | 2.3229 | 0.1680 | |
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| 2.1013 | 1.57 | 800 | 2.1519 | 0.2004 | |
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| 2.0094 | 1.77 | 900 | 2.0611 | 0.2146 | |
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| 2.0387 | 1.96 | 1000 | 2.0413 | 0.2210 | |
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| 2.0032 | 2.16 | 1100 | 1.9758 | 0.2618 | |
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| 1.986 | 2.36 | 1200 | 1.9238 | 0.2638 | |
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| 2.0885 | 2.55 | 1300 | 1.8944 | 0.2942 | |
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| 1.8808 | 2.75 | 1400 | 1.9330 | 0.2868 | |
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| 1.915 | 2.95 | 1500 | 1.8919 | 0.2814 | |
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| 1.958 | 3.14 | 1600 | 1.8762 | 0.3114 | |
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| 1.9001 | 3.34 | 1700 | 1.8389 | 0.3232 | |
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| 1.8572 | 3.54 | 1800 | 1.7978 | 0.3487 | |
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| 1.9969 | 3.73 | 1900 | 1.9371 | 0.3089 | |
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| 1.9186 | 3.93 | 2000 | 1.8055 | 0.3502 | |
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| 1.7591 | 4.13 | 2100 | 1.7695 | 0.3428 | |
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| 1.8368 | 4.32 | 2200 | 1.7498 | 0.3502 | |
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| 1.9842 | 4.52 | 2300 | 1.8049 | 0.3193 | |
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| 1.7606 | 4.72 | 2400 | 1.6730 | 0.3954 | |
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| 1.7787 | 4.91 | 2500 | 1.7104 | 0.3777 | |
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| 1.6377 | 5.11 | 2600 | 1.6647 | 0.3870 | |
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| 1.8834 | 5.3 | 2700 | 1.6325 | 0.3973 | |
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| 1.6149 | 5.5 | 2800 | 1.6722 | 0.3787 | |
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| 1.7038 | 5.7 | 2900 | 1.6425 | 0.3973 | |
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| 1.682 | 5.89 | 3000 | 1.5927 | 0.4180 | |
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| 1.6326 | 6.09 | 3100 | 1.4982 | 0.4622 | |
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| 1.5687 | 6.29 | 3200 | 1.4440 | 0.4774 | |
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| 1.3637 | 6.48 | 3300 | 1.4477 | 0.4877 | |
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| 1.4079 | 6.68 | 3400 | 1.3827 | 0.5020 | |
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| 1.3721 | 6.88 | 3500 | 1.4069 | 0.5010 | |
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| 1.5675 | 7.07 | 3600 | 1.3595 | 0.5083 | |
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| 1.5725 | 7.27 | 3700 | 1.3790 | 0.4956 | |
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| 1.4522 | 7.47 | 3800 | 1.3116 | 0.5378 | |
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| 1.4692 | 7.66 | 3900 | 1.3729 | 0.4980 | |
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| 1.5073 | 7.86 | 4000 | 1.3799 | 0.5216 | |
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| 1.2529 | 8.06 | 4100 | 1.2706 | 0.5486 | |
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| 1.3727 | 8.25 | 4200 | 1.2519 | 0.5535 | |
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| 1.2451 | 8.45 | 4300 | 1.2595 | 0.5648 | |
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| 1.339 | 8.64 | 4400 | 1.3614 | 0.5172 | |
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| 1.2858 | 8.84 | 4500 | 1.3028 | 0.5393 | |
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| 1.1039 | 9.04 | 4600 | 1.2309 | 0.5771 | |
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| 1.0351 | 9.23 | 4700 | 1.2678 | 0.5609 | |
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| 1.1125 | 9.43 | 4800 | 1.2786 | 0.5624 | |
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| 1.1667 | 9.63 | 4900 | 1.2131 | 0.5840 | |
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| 1.1386 | 9.82 | 5000 | 1.1359 | 0.6154 | |
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| 1.1888 | 10.02 | 5100 | 1.1309 | 0.6041 | |
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| 1.1777 | 10.22 | 5200 | 1.1288 | 0.6287 | |
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| 1.3693 | 10.41 | 5300 | 1.3827 | 0.5182 | |
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| 1.1016 | 10.61 | 5400 | 1.2255 | 0.5594 | |
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| 1.1527 | 10.81 | 5500 | 1.0772 | 0.6434 | |
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| 1.1039 | 11.0 | 5600 | 1.1032 | 0.6100 | |
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| 1.2502 | 11.2 | 5700 | 1.1230 | 0.6169 | |
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| 1.0818 | 11.39 | 5800 | 1.0750 | 0.6302 | |
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| 1.0872 | 11.59 | 5900 | 1.0397 | 0.6331 | |
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| 1.0425 | 11.79 | 6000 | 1.0231 | 0.6483 | |
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| 1.0791 | 11.98 | 6100 | 1.0250 | 0.6636 | |
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| 0.9736 | 12.18 | 6200 | 1.0879 | 0.6267 | |
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| 0.9788 | 12.38 | 6300 | 1.1334 | 0.5968 | |
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| 0.8982 | 12.57 | 6400 | 0.9934 | 0.6528 | |
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| 1.077 | 12.77 | 6500 | 0.9698 | 0.6812 | |
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| 1.0347 | 12.97 | 6600 | 1.0265 | 0.6513 | |
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| 0.9159 | 13.16 | 6700 | 0.9442 | 0.6788 | |
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| 1.1187 | 13.36 | 6800 | 0.9738 | 0.6685 | |
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| 0.9624 | 13.56 | 6900 | 1.0008 | 0.6699 | |
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| 0.922 | 13.75 | 7000 | 0.9502 | 0.6906 | |
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| 0.9317 | 13.95 | 7100 | 0.9687 | 0.6758 | |
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| 0.9979 | 14.15 | 7200 | 0.9869 | 0.6768 | |
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| 0.8362 | 14.34 | 7300 | 0.9220 | 0.6994 | |
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| 0.8449 | 14.54 | 7400 | 0.9181 | 0.6861 | |
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| 0.9678 | 14.73 | 7500 | 0.9789 | 0.6729 | |
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| 0.9119 | 14.93 | 7600 | 0.8879 | 0.7009 | |
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| 0.9517 | 15.13 | 7700 | 0.8816 | 0.6994 | |
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| 0.9688 | 15.32 | 7800 | 0.8803 | 0.7117 | |
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| 0.8625 | 15.52 | 7900 | 0.8782 | 0.7038 | |
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| 0.9121 | 15.72 | 8000 | 0.8225 | 0.7191 | |
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| 0.9035 | 15.91 | 8100 | 0.8649 | 0.7087 | |
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| 0.8762 | 16.11 | 8200 | 0.8427 | 0.7102 | |
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| 0.7708 | 16.31 | 8300 | 0.8685 | 0.7117 | |
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| 0.8893 | 16.5 | 8400 | 0.8178 | 0.7264 | |
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| 0.9584 | 16.7 | 8500 | 0.8709 | 0.7092 | |
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| 0.757 | 16.9 | 8600 | 0.8244 | 0.7254 | |
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| 0.8184 | 17.09 | 8700 | 0.8128 | 0.7240 | |
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| 0.8858 | 17.29 | 8800 | 0.8360 | 0.7156 | |
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| 0.7116 | 17.49 | 8900 | 0.7952 | 0.7279 | |
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| 0.9579 | 17.68 | 9000 | 0.8263 | 0.7274 | |
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| 0.7037 | 17.88 | 9100 | 0.7884 | 0.7348 | |
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| 1.0359 | 18.07 | 9200 | 0.8118 | 0.7402 | |
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| 1.067 | 18.27 | 9300 | 0.8203 | 0.7186 | |
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| 0.8503 | 18.47 | 9400 | 0.7918 | 0.7362 | |
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| 0.8552 | 18.66 | 9500 | 0.7972 | 0.7382 | |
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| 0.7498 | 18.86 | 9600 | 0.8038 | 0.7343 | |
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| 0.8542 | 19.06 | 9700 | 0.7799 | 0.7333 | |
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| 0.9539 | 19.25 | 9800 | 0.7795 | 0.7333 | |
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| 0.7369 | 19.45 | 9900 | 0.8103 | 0.7269 | |
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| 0.6637 | 19.65 | 10000 | 0.7597 | 0.7441 | |
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| 0.6712 | 19.84 | 10100 | 0.7557 | 0.7475 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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